Overview

Dataset statistics

Number of variables19
Number of observations78503
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.0 MiB
Average record size in memory160.0 B

Variable types

TimeSeries16
Numeric3

Alerts

Physical Reads Per Sec is highly overall correlated with Physical Writes Per Sec and 7 other fieldsHigh correlation
Physical Writes Per Sec is highly overall correlated with Physical Reads Per Sec and 3 other fieldsHigh correlation
Hard Parse Count Per Sec is highly overall correlated with User Rollbacks Per Sec and 7 other fieldsHigh correlation
User Rollbacks Per Sec is highly overall correlated with Hard Parse Count Per Sec and 4 other fieldsHigh correlation
Logical Reads Per Sec is highly overall correlated with Physical Reads Per Sec and 12 other fieldsHigh correlation
I/O Megabytes per Second is highly overall correlated with Physical Reads Per Sec and 10 other fieldsHigh correlation
User Commits Per Sec is highly overall correlated with Logical Reads Per Sec and 9 other fieldsHigh correlation
Executions Per Sec is highly overall correlated with Logical Reads Per Sec and 9 other fieldsHigh correlation
Redo Generated Per Sec is highly overall correlated with Physical Writes Per Sec and 8 other fieldsHigh correlation
DB Block Changes Per Sec is highly overall correlated with Physical Writes Per Sec and 8 other fieldsHigh correlation
CPU Usage Per Sec is highly overall correlated with Physical Reads Per Sec and 11 other fieldsHigh correlation
Average Active Sessions is highly overall correlated with Physical Reads Per Sec and 13 other fieldsHigh correlation
Host CPU Usage Per Sec is highly overall correlated with Physical Reads Per Sec and 12 other fieldsHigh correlation
Consistent Read Gets Per Sec is highly overall correlated with Physical Reads Per Sec and 10 other fieldsHigh correlation
Logons Per Sec is highly overall correlated with User Calls Per SecHigh correlation
User Calls Per Sec is highly overall correlated with Hard Parse Count Per Sec and 9 other fieldsHigh correlation
Database Time Per Sec is highly overall correlated with Physical Reads Per Sec and 13 other fieldsHigh correlation
Physical Reads Per Sec is non stationaryNon stationary
Physical Writes Per Sec is non stationaryNon stationary
User Rollbacks Per Sec is non stationaryNon stationary
Logical Reads Per Sec is non stationaryNon stationary
I/O Megabytes per Second is non stationaryNon stationary
DBWR Checkpoints Per Sec is non stationaryNon stationary
User Commits Per Sec is non stationaryNon stationary
Executions Per Sec is non stationaryNon stationary
Redo Generated Per Sec is non stationaryNon stationary
DB Block Changes Per Sec is non stationaryNon stationary
CPU Usage Per Sec is non stationaryNon stationary
Average Active Sessions is non stationaryNon stationary
Host CPU Usage Per Sec is non stationaryNon stationary
Consistent Read Gets Per Sec is non stationaryNon stationary
User Calls Per Sec is non stationaryNon stationary
Database Time Per Sec is non stationaryNon stationary
Physical Reads Per Sec is seasonalSeasonal
Physical Writes Per Sec is seasonalSeasonal
User Rollbacks Per Sec is seasonalSeasonal
Logical Reads Per Sec is seasonalSeasonal
I/O Megabytes per Second is seasonalSeasonal
DBWR Checkpoints Per Sec is seasonalSeasonal
User Commits Per Sec is seasonalSeasonal
Executions Per Sec is seasonalSeasonal
Redo Generated Per Sec is seasonalSeasonal
DB Block Changes Per Sec is seasonalSeasonal
CPU Usage Per Sec is seasonalSeasonal
Average Active Sessions is seasonalSeasonal
Host CPU Usage Per Sec is seasonalSeasonal
Consistent Read Gets Per Sec is seasonalSeasonal
User Calls Per Sec is seasonalSeasonal
Database Time Per Sec is seasonalSeasonal
Enqueue Waits Per Sec is highly skewed (γ1 = 20.74190144)Skewed
Redo Generated Per Sec has unique valuesUnique
Consistent Read Gets Per Sec has unique valuesUnique
Database Time Per Sec has unique valuesUnique
DBWR Checkpoints Per Sec has 2487 (3.2%) zerosZeros

Reproduction

Analysis started2023-03-23 15:47:31.604147
Analysis finished2023-03-23 15:54:51.645541
Duration7 minutes and 20.04 seconds
Software versionydata-profiling vv4.1.1
Download configurationconfig.json

Variables

Physical Reads Per Sec
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct78495
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29548.346
Minimum0
Maximum414613.47
Zeros1
Zeros (%)< 0.1%
Memory size1.2 MiB
2023-03-23T15:54:52.196713image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2100.7743
Q110480.828
median25204.96
Q341068.523
95-th percentile75396.082
Maximum414613.47
Range414613.47
Interquartile range (IQR)30587.695

Descriptive statistics

Standard deviation24886.022
Coefficient of variation (CV)0.84221372
Kurtosis7.7589807
Mean29548.346
Median Absolute Deviation (MAD)15192.917
Skewness1.8498473
Sum2.3196338 × 109
Variance6.1931409 × 108
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0
2023-03-23T15:54:52.587299image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2970.121131 2
 
< 0.1%
38637.65277 2
 
< 0.1%
11300 2
 
< 0.1%
12183.56753 2
 
< 0.1%
4693.500589 2
 
< 0.1%
2650.496382 2
 
< 0.1%
5744.66148 2
 
< 0.1%
1320.361766 2
 
< 0.1%
41991.23453 1
 
< 0.1%
31012.06982 1
 
< 0.1%
Other values (78485) 78485
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
12.646913 1
< 0.1%
16.846116 1
< 0.1%
30.131004 1
< 0.1%
31.830986 1
< 0.1%
33.057167 1
< 0.1%
33.587407 1
< 0.1%
34.39758 1
< 0.1%
34.561669 1
< 0.1%
34.687448 1
< 0.1%
ValueCountFrequency (%)
414613.468 1
< 0.1%
413700.3877 1
< 0.1%
391438.1369 1
< 0.1%
324813.6725 1
< 0.1%
294994.8875 1
< 0.1%
282045.9577 1
< 0.1%
268545.0958 1
< 0.1%
268161.6029 1
< 0.1%
265436.3667 1
< 0.1%
257190.6918 1
< 0.1%
2023-03-23T15:54:53.851428image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
ACF and PACF

Physical Writes Per Sec
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct78257
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2320.8767
Minimum0.462963
Maximum86166.522
Zeros0
Zeros (%)0.0%
Memory size1.2 MiB
2023-03-23T15:54:54.329545image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum0.462963
5-th percentile151.23205
Q1536.34561
median978.0117
Q32181.8151
95-th percentile8935.1099
Maximum86166.522
Range86166.059
Interquartile range (IQR)1645.4695

Descriptive statistics

Standard deviation4185.4442
Coefficient of variation (CV)1.8033893
Kurtosis38.368533
Mean2320.8767
Median Absolute Deviation (MAD)576.17544
Skewness5.1174596
Sum1.8219578 × 108
Variance17517943
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0
2023-03-23T15:54:54.807678image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
366.666667 3
 
< 0.1%
325.222846 2
 
< 0.1%
712.506278 2
 
< 0.1%
715.370432 2
 
< 0.1%
1463.636364 2
 
< 0.1%
1068.827264 2
 
< 0.1%
1289.926618 2
 
< 0.1%
365.811106 2
 
< 0.1%
1561.362115 2
 
< 0.1%
877.733333 2
 
< 0.1%
Other values (78247) 78482
> 99.9%
ValueCountFrequency (%)
0.462963 1
< 0.1%
0.512651 1
< 0.1%
0.573937 1
< 0.1%
0.806858 1
< 0.1%
0.92437 1
< 0.1%
6.740459 1
< 0.1%
7.619835 1
< 0.1%
7.669421 1
< 0.1%
7.918628 1
< 0.1%
8.086752 1
< 0.1%
ValueCountFrequency (%)
86166.52196 1
< 0.1%
65304.69499 1
< 0.1%
61672.68997 1
< 0.1%
61672.21201 1
< 0.1%
61499.25738 1
< 0.1%
61123.85352 1
< 0.1%
61098.58381 1
< 0.1%
59607.10877 1
< 0.1%
59551.80602 1
< 0.1%
59509.48478 1
< 0.1%
2023-03-23T15:54:55.779107image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
ACF and PACF

Hard Parse Count Per Sec
Real number (ℝ)

Distinct56641
Distinct (%)72.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.7731629
Minimum0
Maximum519.63685
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2023-03-23T15:54:56.153538image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.439682
Q13.991932
median7.380753
Q311.629829
95-th percentile18.804175
Maximum519.63685
Range519.63685
Interquartile range (IQR)7.6378975

Descriptive statistics

Standard deviation10.726805
Coefficient of variation (CV)1.2226839
Kurtosis667.24177
Mean8.7731629
Median Absolute Deviation (MAD)3.686999
Skewness19.298761
Sum688719.6
Variance115.06434
MonotonicityNot monotonic
2023-03-23T15:54:56.350636image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 11
 
< 0.1%
3.225806 10
 
< 0.1%
9.090909 10
 
< 0.1%
2.857143 9
 
< 0.1%
5.882353 9
 
< 0.1%
6.25 8
 
< 0.1%
5.149279 8
 
< 0.1%
3.808725 7
 
< 0.1%
11.764706 7
 
< 0.1%
5.263158 7
 
< 0.1%
Other values (56631) 78417
99.9%
ValueCountFrequency (%)
0 4
< 0.1%
0.016548 1
 
< 0.1%
0.01657 1
 
< 0.1%
0.016581 1
 
< 0.1%
0.01679 1
 
< 0.1%
0.016807 1
 
< 0.1%
0.016832 1
 
< 0.1%
0.033047 1
 
< 0.1%
0.033091 1
 
< 0.1%
0.033151 1
 
< 0.1%
ValueCountFrequency (%)
519.636853 1
< 0.1%
516.517857 1
< 0.1%
514.863081 1
< 0.1%
511.879195 1
< 0.1%
511.22668 1
< 0.1%
510.760852 1
< 0.1%
492.012726 1
< 0.1%
436.718619 1
< 0.1%
428.032922 1
< 0.1%
423.044646 1
< 0.1%

User Rollbacks Per Sec
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct74394
Distinct (%)94.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.129466
Minimum0
Maximum1879.845
Zeros95
Zeros (%)0.1%
Memory size1.2 MiB
2023-03-23T15:54:56.632344image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.0517162
Q121.243827
median45.151007
Q382.756021
95-th percentile156.45281
Maximum1879.845
Range1879.845
Interquartile range (IQR)61.512194

Descriptive statistics

Standard deviation63.728616
Coefficient of variation (CV)1.0598567
Kurtosis116.38684
Mean60.129466
Median Absolute Deviation (MAD)27.994227
Skewness6.9562695
Sum4720343.5
Variance4061.3364
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0
2023-03-23T15:54:56.884745image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 95
 
0.1%
0.016795 8
 
< 0.1%
50 7
 
< 0.1%
40 7
 
< 0.1%
0.016521 5
 
< 0.1%
0.016779 5
 
< 0.1%
0.033591 5
 
< 0.1%
0.016507 5
 
< 0.1%
0.016804 5
 
< 0.1%
0.033574 5
 
< 0.1%
Other values (74384) 78356
99.8%
ValueCountFrequency (%)
0 95
0.1%
0.016491 1
 
< 0.1%
0.016496 1
 
< 0.1%
0.016499 1
 
< 0.1%
0.016502 2
 
< 0.1%
0.016504 3
 
< 0.1%
0.016507 5
 
< 0.1%
0.01651 3
 
< 0.1%
0.016513 1
 
< 0.1%
0.016515 2
 
< 0.1%
ValueCountFrequency (%)
1879.844961 1
< 0.1%
1796.06497 1
< 0.1%
1730.202336 1
< 0.1%
1650.776423 1
< 0.1%
1603.195425 1
< 0.1%
1536.440258 1
< 0.1%
1533.659194 1
< 0.1%
1511.297071 1
< 0.1%
1487.592468 1
< 0.1%
1476.634742 1
< 0.1%
2023-03-23T15:54:57.984075image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
ACF and PACF

Logical Reads Per Sec
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct78502
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean681622.77
Minimum6158.9226
Maximum5263690.7
Zeros0
Zeros (%)0.0%
Memory size1.2 MiB
2023-03-23T15:54:58.774228image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum6158.9226
5-th percentile164618.05
Q1392831.93
median679735.85
Q3919142.56
95-th percentile1247587.2
Maximum5263690.7
Range5257531.8
Interquartile range (IQR)526310.63

Descriptive statistics

Standard deviation358216.22
Coefficient of variation (CV)0.52553442
Kurtosis5.3101796
Mean681622.77
Median Absolute Deviation (MAD)262541.72
Skewness0.94587819
Sum5.3509432 × 1010
Variance1.2831886 × 1011
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value8.04682694 × 10-30
2023-03-23T15:54:59.023323image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
910821.5506 2
 
< 0.1%
154055.3793 1
 
< 0.1%
810591.0992 1
 
< 0.1%
787702.827 1
 
< 0.1%
799405.2692 1
 
< 0.1%
836719.1066 1
 
< 0.1%
758114.9749 1
 
< 0.1%
702362.5313 1
 
< 0.1%
756856.3313 1
 
< 0.1%
712854.0486 1
 
< 0.1%
Other values (78492) 78492
> 99.9%
ValueCountFrequency (%)
6158.922559 1
< 0.1%
9189.249627 1
< 0.1%
9422.642762 1
< 0.1%
9695.390483 1
< 0.1%
12277.80172 1
< 0.1%
12550.9761 1
< 0.1%
12604.4981 1
< 0.1%
12731.5363 1
< 0.1%
13291.15761 1
< 0.1%
13342.91478 1
< 0.1%
ValueCountFrequency (%)
5263690.682 1
< 0.1%
5170981.104 1
< 0.1%
5068388.295 1
< 0.1%
4901475.542 1
< 0.1%
4770479.997 1
< 0.1%
4636225.33 1
< 0.1%
4567017.522 1
< 0.1%
4505719.394 1
< 0.1%
4431446.823 1
< 0.1%
4348935.622 1
< 0.1%
2023-03-23T15:54:59.819828image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
ACF and PACF

I/O Megabytes per Second
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct77544
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean276.64787
Minimum0.530592
Maximum3240.0034
Zeros0
Zeros (%)0.0%
Memory size1.2 MiB
2023-03-23T15:55:00.248877image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum0.530592
5-th percentile26.177025
Q1114.97923
median242.43837
Q3374.99793
95-th percentile678.7411
Maximum3240.0034
Range3239.4728
Interquartile range (IQR)260.0187

Descriptive statistics

Standard deviation215.68161
Coefficient of variation (CV)0.77962504
Kurtosis6.3545659
Mean276.64787
Median Absolute Deviation (MAD)129.65099
Skewness1.7163961
Sum21717688
Variance46518.555
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0
2023-03-23T15:55:00.490336image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200 4
 
< 0.1%
53.293611 3
 
< 0.1%
32.886905 3
 
< 0.1%
366.666667 3
 
< 0.1%
88.888889 3
 
< 0.1%
17.952172 3
 
< 0.1%
258.514553 3
 
< 0.1%
21.332436 3
 
< 0.1%
301.023318 3
 
< 0.1%
153.054822 3
 
< 0.1%
Other values (77534) 78472
> 99.9%
ValueCountFrequency (%)
0.530592 1
< 0.1%
0.61309 1
< 0.1%
0.629452 1
< 0.1%
0.679821 1
< 0.1%
0.824222 1
< 0.1%
0.82575 1
< 0.1%
0.909241 1
< 0.1%
1.024793 1
< 0.1%
1.043911 1
< 0.1%
1.247682 1
< 0.1%
ValueCountFrequency (%)
3240.003372 1
< 0.1%
3227.138047 1
< 0.1%
3050.058853 1
< 0.1%
2610.059662 1
< 0.1%
2376.496083 1
< 0.1%
2374.396537 1
< 0.1%
2352.107728 1
< 0.1%
2340.221707 1
< 0.1%
2265.800067 1
< 0.1%
2235.259161 1
< 0.1%
2023-03-23T15:55:01.518137image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
ACF and PACF

DBWR Checkpoints Per Sec
Numeric time series

NON STATIONARY  SEASONAL  ZEROS 

Distinct43707
Distinct (%)55.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.245354
Minimum0
Maximum758.49629
Zeros2487
Zeros (%)3.2%
Memory size1.2 MiB
2023-03-23T15:55:02.234439image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.016804
Q10.428831
median1.614802
Q35.034172
95-th percentile132.23767
Maximum758.49629
Range758.49629
Interquartile range (IQR)4.605341

Descriptive statistics

Standard deviation50.940552
Coefficient of variation (CV)2.5161601
Kurtosis21.191427
Mean20.245354
Median Absolute Deviation (MAD)1.431132
Skewness3.7661346
Sum1589321
Variance2594.9398
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.043847653 × 10-30
2023-03-23T15:55:02.491159image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2487
 
3.2%
0.016804 39
 
< 0.1%
0.016787 34
 
< 0.1%
0.016784 32
 
< 0.1%
0.033512 31
 
< 0.1%
0.016801 31
 
< 0.1%
0.033047 30
 
< 0.1%
0.016762 29
 
< 0.1%
0.016767 29
 
< 0.1%
0.016521 28
 
< 0.1%
Other values (43697) 75733
96.5%
ValueCountFrequency (%)
0 2487
3.2%
0.016399 1
 
< 0.1%
0.016412 1
 
< 0.1%
0.016423 1
 
< 0.1%
0.016431 1
 
< 0.1%
0.016434 1
 
< 0.1%
0.016437 3
 
< 0.1%
0.016439 1
 
< 0.1%
0.016442 5
 
< 0.1%
0.016445 2
 
< 0.1%
ValueCountFrequency (%)
758.496291 1
< 0.1%
751.193238 1
< 0.1%
739.615644 1
< 0.1%
737.493784 1
< 0.1%
736.929041 1
< 0.1%
734.415907 1
< 0.1%
731.443726 1
< 0.1%
729.794405 1
< 0.1%
728.227165 1
< 0.1%
721.769166 1
< 0.1%
2023-03-23T15:55:03.535056image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
ACF and PACF

User Commits Per Sec
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct77956
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean757.28228
Minimum0
Maximum32718.041
Zeros1
Zeros (%)< 0.1%
Memory size1.2 MiB
2023-03-23T15:55:03.959958image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile95.496167
Q1310.44645
median580.65422
Q3797.56021
95-th percentile1917.0589
Maximum32718.041
Range32718.041
Interquartile range (IQR)487.11376

Descriptive statistics

Standard deviation1085.1564
Coefficient of variation (CV)1.4329615
Kurtosis60.88583
Mean757.28228
Median Absolute Deviation (MAD)246.06261
Skewness6.1732464
Sum59448931
Variance1177564.3
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0
2023-03-23T15:55:04.193950image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200 4
 
< 0.1%
315.558912 3
 
< 0.1%
212.920792 3
 
< 0.1%
933.333333 3
 
< 0.1%
396.278914 3
 
< 0.1%
752.166749 3
 
< 0.1%
368.436975 3
 
< 0.1%
738.751255 2
 
< 0.1%
874.958208 2
 
< 0.1%
628.504281 2
 
< 0.1%
Other values (77946) 78475
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1.890614 1
< 0.1%
1.975432 1
< 0.1%
2.008299 1
< 0.1%
2.124834 1
< 0.1%
2.207469 1
< 0.1%
2.257261 1
< 0.1%
2.290456 1
< 0.1%
2.307054 1
< 0.1%
2.324037 1
< 0.1%
ValueCountFrequency (%)
32718.04132 1
< 0.1%
29225.34269 1
< 0.1%
27658.40357 1
< 0.1%
23356.53775 1
< 0.1%
21386.32937 1
< 0.1%
19681.42692 1
< 0.1%
18142.00234 1
< 0.1%
15858.80181 1
< 0.1%
15626.65885 1
< 0.1%
15541.28364 1
< 0.1%
2023-03-23T15:55:05.021521image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
ACF and PACF

Enqueue Waits Per Sec
Real number (ℝ)

Distinct76796
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean159.64771
Minimum0.184224
Maximum10809.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2023-03-23T15:55:05.395457image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum0.184224
5-th percentile16.702894
Q178.260146
median145.54896
Q3199.34859
95-th percentile380.06528
Maximum10809.95
Range10809.766
Interquartile range (IQR)121.08844

Descriptive statistics

Standard deviation151.23999
Coefficient of variation (CV)0.94733578
Kurtosis1077.3181
Mean159.64771
Median Absolute Deviation (MAD)60.719548
Skewness20.741901
Sum12532824
Variance22873.534
MonotonicityNot monotonic
2023-03-23T15:55:05.599068image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200 7
 
< 0.1%
137.5 4
 
< 0.1%
133.333333 4
 
< 0.1%
157.465392 3
 
< 0.1%
158.823529 3
 
< 0.1%
168.144554 3
 
< 0.1%
186.666667 3
 
< 0.1%
94.117647 3
 
< 0.1%
141.666667 3
 
< 0.1%
163.145579 3
 
< 0.1%
Other values (76786) 78467
> 99.9%
ValueCountFrequency (%)
0.184224 1
< 0.1%
0.229961 1
< 0.1%
0.233996 1
< 0.1%
0.235294 1
< 0.1%
0.250376 1
< 0.1%
0.301255 1
< 0.1%
0.302928 1
< 0.1%
0.329435 1
< 0.1%
0.333556 1
< 0.1%
0.34728 2
< 0.1%
ValueCountFrequency (%)
10809.95041 1
< 0.1%
10088.69018 1
< 0.1%
8446.534326 1
< 0.1%
8099.395466 1
< 0.1%
7571.758557 1
< 0.1%
7314.657602 1
< 0.1%
7059.099189 1
< 0.1%
6800.960106 1
< 0.1%
5750.717058 1
< 0.1%
5226.315789 1
< 0.1%

Executions Per Sec
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct78462
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33287.103
Minimum0
Maximum297473.55
Zeros35
Zeros (%)< 0.1%
Memory size1.2 MiB
2023-03-23T15:55:06.141294image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7371.1909
Q117979.492
median30602.874
Q342770.942
95-th percentile66363.835
Maximum297473.55
Range297473.55
Interquartile range (IQR)24791.45

Descriptive statistics

Standard deviation23332.76
Coefficient of variation (CV)0.70095497
Kurtosis15.405955
Mean33287.103
Median Absolute Deviation (MAD)12414.378
Skewness2.8480832
Sum2.6131375 × 109
Variance5.444177 × 108
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0
2023-03-23T15:55:06.378923image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 35
 
< 0.1%
11074.63479 2
 
< 0.1%
18089.68934 2
 
< 0.1%
50940.53512 2
 
< 0.1%
13745.13854 2
 
< 0.1%
41525.91168 2
 
< 0.1%
26460.34108 2
 
< 0.1%
41464.75904 2
 
< 0.1%
19780.06941 1
 
< 0.1%
35148.75105 1
 
< 0.1%
Other values (78452) 78452
99.9%
ValueCountFrequency (%)
0 35
< 0.1%
219.574755 1
 
< 0.1%
238.53815 1
 
< 0.1%
263.136929 1
 
< 0.1%
264.475432 1
 
< 0.1%
265.338645 1
 
< 0.1%
269.769753 1
 
< 0.1%
272.215768 1
 
< 0.1%
272.315353 1
 
< 0.1%
288.649187 1
 
< 0.1%
ValueCountFrequency (%)
297473.5457 1
< 0.1%
289907.4844 1
< 0.1%
280832.0101 1
< 0.1%
275776.9167 1
< 0.1%
272277.691 1
< 0.1%
268272.7001 1
< 0.1%
266789.3249 1
< 0.1%
260734.3005 1
< 0.1%
259853.1869 1
< 0.1%
255724.9917 1
< 0.1%
2023-03-23T15:55:07.225829image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
ACF and PACF

Redo Generated Per Sec
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL  UNIQUE 

Distinct78503
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4766772.7
Minimum13901.66
Maximum1.4612018 × 108
Zeros0
Zeros (%)0.0%
Memory size1.2 MiB
2023-03-23T15:55:07.881771image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum13901.66
5-th percentile510434.76
Q11762806.6
median3078103.4
Q34721574.7
95-th percentile16542621
Maximum1.4612018 × 108
Range1.4610628 × 108
Interquartile range (IQR)2958768.1

Descriptive statistics

Standard deviation5896860.6
Coefficient of variation (CV)1.2370761
Kurtosis34.800476
Mean4766772.7
Median Absolute Deviation (MAD)1438135.1
Skewness4.3318546
Sum3.7420596 × 1011
Variance3.4772964 × 1013
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0
2023-03-23T15:55:08.132990image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
414887.5227 1
 
< 0.1%
3797452.367 1
 
< 0.1%
2715027.768 1
 
< 0.1%
2724349.415 1
 
< 0.1%
2647577.514 1
 
< 0.1%
2703213.065 1
 
< 0.1%
2577719.244 1
 
< 0.1%
2506052.692 1
 
< 0.1%
2941851.668 1
 
< 0.1%
2900993.564 1
 
< 0.1%
Other values (78493) 78493
> 99.9%
ValueCountFrequency (%)
13901.66003 1
< 0.1%
16837.17842 1
< 0.1%
17390.01848 1
< 0.1%
18337.35737 1
< 0.1%
19760.90051 1
< 0.1%
20219.80165 1
< 0.1%
20238.82275 1
< 0.1%
20322.4693 1
< 0.1%
21513.13265 1
< 0.1%
22179.1411 1
< 0.1%
ValueCountFrequency (%)
146120177.6 1
< 0.1%
107072962.1 1
< 0.1%
99705854.39 1
< 0.1%
95113839.73 1
< 0.1%
91062848.71 1
< 0.1%
89849617.93 1
< 0.1%
88651947.28 1
< 0.1%
87683785.42 1
< 0.1%
86785413.09 1
< 0.1%
86049779.44 1
< 0.1%
2023-03-23T15:55:09.026869image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
ACF and PACF

DB Block Changes Per Sec
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct78469
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17874.018
Minimum0
Maximum566312.84
Zeros16
Zeros (%)< 0.1%
Memory size1.2 MiB
2023-03-23T15:55:09.443081image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2057.3751
Q16804.948
median10964.16
Q317787.27
95-th percentile62703.389
Maximum566312.84
Range566312.84
Interquartile range (IQR)10982.322

Descriptive statistics

Standard deviation22590.487
Coefficient of variation (CV)1.263873
Kurtosis29.90537
Mean17874.018
Median Absolute Deviation (MAD)4943.2458
Skewness4.0110843
Sum1.403164 × 109
Variance5.1033012 × 108
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0
2023-03-23T15:55:09.668920image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16
 
< 0.1%
1677.281007 2
 
< 0.1%
11899.0985 2
 
< 0.1%
2367.472673 2
 
< 0.1%
9338.014326 2
 
< 0.1%
8265.198459 2
 
< 0.1%
5901.841621 2
 
< 0.1%
4117.436662 2
 
< 0.1%
7892.258926 2
 
< 0.1%
10253.57918 2
 
< 0.1%
Other values (78459) 78469
> 99.9%
ValueCountFrequency (%)
0 16
< 0.1%
64.947068 1
 
< 0.1%
65.156276 1
 
< 0.1%
71.995293 1
 
< 0.1%
74.053785 1
 
< 0.1%
83.476679 1
 
< 0.1%
87.236515 1
 
< 0.1%
87.531089 1
 
< 0.1%
88.971561 1
 
< 0.1%
95.696077 1
 
< 0.1%
ValueCountFrequency (%)
566312.8446 1
< 0.1%
562221.7712 1
< 0.1%
438705.3539 1
< 0.1%
428464.3958 1
< 0.1%
425230.9796 1
< 0.1%
377010.8081 1
< 0.1%
351541.6947 1
< 0.1%
302230.0536 1
< 0.1%
291526.1763 1
< 0.1%
286195.4477 1
< 0.1%
2023-03-23T15:55:10.482033image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
ACF and PACF

CPU Usage Per Sec
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct78502
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1699.6032
Minimum21.600052
Maximum7479.7115
Zeros0
Zeros (%)0.0%
Memory size1.2 MiB
2023-03-23T15:55:11.215947image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum21.600052
5-th percentile381.61787
Q1949.73488
median1556.7578
Q32349.763
95-th percentile3412.7622
Maximum7479.7115
Range7458.1114
Interquartile range (IQR)1400.0281

Descriptive statistics

Standard deviation959.82045
Coefficient of variation (CV)0.56473207
Kurtosis0.34735337
Mean1699.6032
Median Absolute Deviation (MAD)679.28911
Skewness0.67699748
Sum1.3342395 × 108
Variance921255.29
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value5.512401254 × 10-28
2023-03-23T15:55:11.445668image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
998.728908 2
 
< 0.1%
301.369693 1
 
< 0.1%
1929.184422 1
 
< 0.1%
2340.937938 1
 
< 0.1%
2015.844418 1
 
< 0.1%
2202.516281 1
 
< 0.1%
2248.158474 1
 
< 0.1%
2264.392714 1
 
< 0.1%
2206.317832 1
 
< 0.1%
2157.775691 1
 
< 0.1%
Other values (78492) 78492
> 99.9%
ValueCountFrequency (%)
21.600052 1
< 0.1%
25.689468 1
< 0.1%
26.228712 1
< 0.1%
29.018482 1
< 0.1%
29.521966 1
< 0.1%
30.170123 1
< 0.1%
30.549265 1
< 0.1%
31.669102 1
< 0.1%
31.673898 1
< 0.1%
31.780983 1
< 0.1%
ValueCountFrequency (%)
7479.711474 1
< 0.1%
7289.768584 1
< 0.1%
7211.416495 1
< 0.1%
7199.863551 1
< 0.1%
7176.045918 1
< 0.1%
7152.557732 1
< 0.1%
7106.699749 1
< 0.1%
7069.575375 1
< 0.1%
7035.729417 1
< 0.1%
6998.494544 1
< 0.1%
2023-03-23T15:55:12.276982image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
ACF and PACF

Average Active Sessions
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct78450
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.284078
Minimum0.302545
Maximum202.85754
Zeros0
Zeros (%)0.0%
Memory size1.2 MiB
2023-03-23T15:55:12.724151image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum0.302545
5-th percentile5.3356702
Q113.88055
median24.678667
Q336.696598
95-th percentile52.803896
Maximum202.85754
Range202.555
Interquartile range (IQR)22.816048

Descriptive statistics

Standard deviation15.168757
Coefficient of variation (CV)0.5771082
Kurtosis0.83344927
Mean26.284078
Median Absolute Deviation (MAD)11.312887
Skewness0.66700191
Sum2063379
Variance230.09119
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.242546326 × 10-28
2023-03-23T15:55:12.962653image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.423404 2
 
< 0.1%
10.703358 2
 
< 0.1%
14.290057 2
 
< 0.1%
36.052369 2
 
< 0.1%
15.296076 2
 
< 0.1%
5.112199 2
 
< 0.1%
11.190764 2
 
< 0.1%
9.449209 2
 
< 0.1%
36.680474 2
 
< 0.1%
7.215089 2
 
< 0.1%
Other values (78440) 78483
> 99.9%
ValueCountFrequency (%)
0.302545 1
< 0.1%
0.324671 1
< 0.1%
0.347824 1
< 0.1%
0.365153 1
< 0.1%
0.376259 1
< 0.1%
0.377535 1
< 0.1%
0.377993 1
< 0.1%
0.380035 1
< 0.1%
0.38443 1
< 0.1%
0.405927 1
< 0.1%
ValueCountFrequency (%)
202.857543 1
< 0.1%
194.78528 1
< 0.1%
151.206537 1
< 0.1%
141.789072 1
< 0.1%
134.468115 1
< 0.1%
132.235121 1
< 0.1%
131.850179 1
< 0.1%
131.549115 1
< 0.1%
127.846127 1
< 0.1%
124.444605 1
< 0.1%
2023-03-23T15:55:13.853568image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
ACF and PACF

Host CPU Usage Per Sec
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct78319
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2868.7751
Minimum0
Maximum9443.1158
Zeros3
Zeros (%)< 0.1%
Memory size1.2 MiB
2023-03-23T15:55:14.543891image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile986.92676
Q11849.2368
median2741.8476
Q33781.9449
95-th percentile5108.0658
Maximum9443.1158
Range9443.1158
Interquartile range (IQR)1932.7081

Descriptive statistics

Standard deviation1296.9744
Coefficient of variation (CV)0.45210041
Kurtosis-0.082931137
Mean2868.7751
Median Absolute Deviation (MAD)956.21709
Skewness0.46044487
Sum2.2520745 × 108
Variance1682142.7
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.187978886 × 10-29
2023-03-23T15:55:14.778149image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3700 3
 
< 0.1%
0 3
 
< 0.1%
3766.025427 2
 
< 0.1%
1085.501243 2
 
< 0.1%
2317.527151 2
 
< 0.1%
1799.496137 2
 
< 0.1%
2955.399835 2
 
< 0.1%
3447.178292 2
 
< 0.1%
3050.883333 2
 
< 0.1%
2398.630137 2
 
< 0.1%
Other values (78309) 78481
> 99.9%
ValueCountFrequency (%)
0 3
< 0.1%
247.717085 1
 
< 0.1%
261.045643 1
 
< 0.1%
261.252744 1
 
< 0.1%
267.363878 1
 
< 0.1%
270.560432 1
 
< 0.1%
273.788181 1
 
< 0.1%
273.979075 1
 
< 0.1%
280.899436 1
 
< 0.1%
281.321601 1
 
< 0.1%
ValueCountFrequency (%)
9443.115762 1
< 0.1%
9442.133693 1
< 0.1%
9409.660791 1
< 0.1%
9407.709177 1
< 0.1%
9377.480472 1
< 0.1%
9326.551323 1
< 0.1%
9303.091241 1
< 0.1%
9206.271777 1
< 0.1%
9077.423109 1
< 0.1%
9024.264584 1
< 0.1%
2023-03-23T15:55:15.574615image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
ACF and PACF

Consistent Read Gets Per Sec
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL  UNIQUE 

Distinct78503
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean650298.78
Minimum0
Maximum5147791.5
Zeros1
Zeros (%)< 0.1%
Memory size1.2 MiB
2023-03-23T15:55:16.004072image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile156672.25
Q1370910.58
median647528.89
Q3881673.01
95-th percentile1188811.4
Maximum5147791.5
Range5147791.5
Interquartile range (IQR)510762.43

Descriptive statistics

Standard deviation345037.65
Coefficient of variation (CV)0.53058327
Kurtosis5.6731849
Mean650298.78
Median Absolute Deviation (MAD)255028.26
Skewness0.97992754
Sum5.1050405 × 1010
Variance1.1905098 × 1011
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.029258087 × 10-29
2023-03-23T15:55:16.237616image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150213.3201 1
 
< 0.1%
790761.2682 1
 
< 0.1%
771303.2285 1
 
< 0.1%
783512.6297 1
 
< 0.1%
822105.6717 1
 
< 0.1%
743032.3283 1
 
< 0.1%
686302.0231 1
 
< 0.1%
742723.1517 1
 
< 0.1%
695228.4325 1
 
< 0.1%
781651.0727 1
 
< 0.1%
Other values (78493) 78493
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
5189.360996 1
< 0.1%
6023.181818 1
< 0.1%
7552.058433 1
< 0.1%
9038.992877 1
< 0.1%
9296.032537 1
< 0.1%
9380.671843 1
< 0.1%
9563.886586 1
< 0.1%
11382.91833 1
< 0.1%
11598.93758 1
< 0.1%
ValueCountFrequency (%)
5147791.453 1
< 0.1%
5049699.715 1
< 0.1%
4953502.901 1
< 0.1%
4782969.36 1
< 0.1%
4662919.311 1
< 0.1%
4530734.449 1
< 0.1%
4453934.809 1
< 0.1%
4397406.789 1
< 0.1%
4318944.711 1
< 0.1%
4222115.074 1
< 0.1%
2023-03-23T15:55:17.055437image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
ACF and PACF

Logons Per Sec
Real number (ℝ)

Distinct56842
Distinct (%)72.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9371367
Minimum0.03308
Maximum1296.3018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2023-03-23T15:55:17.417363image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum0.03308
5-th percentile1.207243
Q13.689514
median6.719169
Q310.868653
95-th percentile18.364216
Maximum1296.3018
Range1296.2687
Interquartile range (IQR)7.179139

Descriptive statistics

Standard deviation36.398309
Coefficient of variation (CV)3.6628568
Kurtosis414.32751
Mean9.9371367
Median Absolute Deviation (MAD)3.442673
Skewness19.139908
Sum780095.04
Variance1324.8369
MonotonicityNot monotonic
2023-03-23T15:55:17.603853image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 10
 
< 0.1%
5.555556 9
 
< 0.1%
3.123451 9
 
< 0.1%
13.043478 8
 
< 0.1%
6.436473 8
 
< 0.1%
4.347826 8
 
< 0.1%
2.635555 8
 
< 0.1%
5.258737 8
 
< 0.1%
2.529762 8
 
< 0.1%
2.620527 7
 
< 0.1%
Other values (56832) 78420
99.9%
ValueCountFrequency (%)
0.03308 1
< 0.1%
0.066105 1
< 0.1%
0.066247 2
< 0.1%
0.066324 1
< 0.1%
0.067002 1
< 0.1%
0.067397 1
< 0.1%
0.082522 1
< 0.1%
0.082535 1
< 0.1%
0.082549 2
< 0.1%
0.082563 1
< 0.1%
ValueCountFrequency (%)
1296.301824 1
< 0.1%
1259.002816 1
< 0.1%
1160.833194 1
< 0.1%
1114.15 1
< 0.1%
1082.963965 1
< 0.1%
1052.517143 1
< 0.1%
1037.541091 1
< 0.1%
1026.048454 1
< 0.1%
1016.292228 1
< 0.1%
873.785595 1
< 0.1%

User Calls Per Sec
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct78460
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31777.211
Minimum0
Maximum140981.08
Zeros35
Zeros (%)< 0.1%
Memory size1.2 MiB
2023-03-23T15:55:18.130433image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9108.6507
Q117472.789
median26684.778
Q345950.498
95-th percentile63685.858
Maximum140981.08
Range140981.08
Interquartile range (IQR)28477.709

Descriptive statistics

Standard deviation17847.133
Coefficient of variation (CV)0.56163307
Kurtosis-0.39749177
Mean31777.211
Median Absolute Deviation (MAD)12484.757
Skewness0.60164346
Sum2.4946064 × 109
Variance3.1852014 × 108
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value3.923270546 × 10-15
2023-03-23T15:55:18.363755image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 35
 
< 0.1%
18088.6843 2
 
< 0.1%
26497.38606 2
 
< 0.1%
11800.83949 2
 
< 0.1%
17092.80081 2
 
< 0.1%
24169.85306 2
 
< 0.1%
13500 2
 
< 0.1%
35889.94815 2
 
< 0.1%
14257.94596 2
 
< 0.1%
11548.99015 2
 
< 0.1%
Other values (78450) 78450
99.9%
ValueCountFrequency (%)
0 35
< 0.1%
540.229497 1
 
< 0.1%
558.435532 1
 
< 0.1%
567.049291 1
 
< 0.1%
645.596639 1
 
< 0.1%
650.738589 1
 
< 0.1%
658.771784 1
 
< 0.1%
707.285098 1
 
< 0.1%
720 1
 
< 0.1%
735.474768 1
 
< 0.1%
ValueCountFrequency (%)
140981.0797 1
< 0.1%
135661.4378 1
< 0.1%
133542.8427 1
< 0.1%
131946.3793 1
< 0.1%
131827.3947 1
< 0.1%
131772.44 1
< 0.1%
131548.909 1
< 0.1%
131039.4833 1
< 0.1%
130749.4704 1
< 0.1%
127054.5868 1
< 0.1%
2023-03-23T15:55:19.229183image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
ACF and PACF

Database Time Per Sec
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL  UNIQUE 

Distinct78503
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2628.4078
Minimum30.254458
Maximum20285.754
Zeros0
Zeros (%)0.0%
Memory size1.2 MiB
2023-03-23T15:55:19.676698image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum30.254458
5-th percentile533.56705
Q11388.0549
median2467.8667
Q33669.6598
95-th percentile5280.3896
Maximum20285.754
Range20255.5
Interquartile range (IQR)2281.6048

Descriptive statistics

Standard deviation1516.8757
Coefficient of variation (CV)0.5771082
Kurtosis0.83344927
Mean2628.4078
Median Absolute Deviation (MAD)1131.2887
Skewness0.66700191
Sum2.063379 × 108
Variance2300911.9
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.24254634 × 10-28
2023-03-23T15:55:19.916049image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
387.990631 1
 
< 0.1%
3118.156356 1
 
< 0.1%
3435.595984 1
 
< 0.1%
3248.814624 1
 
< 0.1%
3437.62684 1
 
< 0.1%
3695.613764 1
 
< 0.1%
3410.9209 1
 
< 0.1%
3628.934036 1
 
< 0.1%
2587.462463 1
 
< 0.1%
3004.708034 1
 
< 0.1%
Other values (78493) 78493
> 99.9%
ValueCountFrequency (%)
30.254458 1
< 0.1%
32.467116 1
< 0.1%
34.782366 1
< 0.1%
36.515259 1
< 0.1%
37.625913 1
< 0.1%
37.753499 1
< 0.1%
37.799273 1
< 0.1%
38.003503 1
< 0.1%
38.443001 1
< 0.1%
40.59273 1
< 0.1%
ValueCountFrequency (%)
20285.7543 1
< 0.1%
19478.52802 1
< 0.1%
15120.65367 1
< 0.1%
14178.90715 1
< 0.1%
13446.81149 1
< 0.1%
13223.51209 1
< 0.1%
13185.01788 1
< 0.1%
13154.91153 1
< 0.1%
12784.61266 1
< 0.1%
12444.46047 1
< 0.1%
2023-03-23T15:55:20.735434image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
ACF and PACF

Interactions

2023-03-23T15:54:47.137696image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:53:50.143973image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:53:53.183302image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:53:56.060828image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:53:58.890046image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:02.161141image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:05.280265image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:08.440557image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:12.007728image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:15.294242image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:18.328052image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:21.384310image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:24.775566image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:27.891996image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:30.890319image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:34.319458image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:37.318481image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:40.587749image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:43.506467image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:47.288978image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:53:50.492602image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:53:53.336804image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:53:56.202978image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:53:59.033096image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:02.302414image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:05.469898image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:08.584758image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:12.167239image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:15.449149image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:18.478297image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:21.523092image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:24.918644image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:28.031613image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:31.043391image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:34.461999image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:37.473573image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:40.734917image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:43.650592image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:47.465231image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:53:50.624613image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:53:53.480794image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:53:56.343333image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:53:59.174890image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:02.439662image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:05.667430image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:08.731955image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:12.318658image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:15.605057image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:18.635443image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:21.665284image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:25.065750image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:28.168834image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:31.188808image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:34.598995image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:37.630980image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:40.918633image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:43.802193image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:47.636894image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:53:50.765257image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:53:53.630539image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:53:56.487236image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:53:59.343916image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:02.584797image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:05.859477image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:08.878633image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:12.475702image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:15.770592image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:18.782084image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:21.828771image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:25.208927image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:28.312077image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:31.340544image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:34.736529image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:37.784716image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:41.073421image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:43.951051image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:47.821438image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:53:50.920095image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:53:53.785552image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:53:56.651369image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:53:59.531736image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:02.734291image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:06.011962image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:09.035883image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:12.637179image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:15.955866image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:18.945497image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:21.981556image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:25.393424image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:28.488149image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:31.506321image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:34.886903image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:37.959066image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:41.228478image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:44.116305image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:47.994531image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:53:51.067654image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:53:53.944485image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:53:56.789081image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:53:59.697227image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:02.883008image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:06.167819image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:09.194468image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:12.799364image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:16.112725image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:19.088087image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:22.133566image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:25.558234image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:28.650768image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:31.665349image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:35.030926image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:38.107817image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:41.375863image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:44.670210image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:48.172712image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:53:51.211659image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:53:54.094601image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:53:56.939497image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:53:59.853320image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:03.042065image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:06.327244image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:09.408182image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:12.977365image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:16.307572image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:19.232048image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:22.289080image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:25.708782image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:28.806625image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:31.835882image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:35.184068image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:38.293746image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:41.527151image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:44.833813image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:48.352834image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:53:51.364158image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:53:54.260310image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
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2023-03-23T15:54:33.989262image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:36.958982image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:40.248546image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:43.202425image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:46.786308image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:50.320882image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:53:53.010775image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:53:55.909986image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:53:58.723637image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:01.994848image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:05.066731image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:08.274694image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:11.826214image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:15.123750image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:18.160234image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:21.223305image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:24.575818image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:27.726996image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:30.731414image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:34.147560image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:37.131669image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:40.420106image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:43.350022image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-03-23T15:54:46.964311image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Correlations

2023-03-23T15:55:21.073758image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Physical Reads Per SecPhysical Writes Per SecHard Parse Count Per SecUser Rollbacks Per SecLogical Reads Per SecI/O Megabytes per SecondDBWR Checkpoints Per SecUser Commits Per SecEnqueue Waits Per SecExecutions Per SecRedo Generated Per SecDB Block Changes Per SecCPU Usage Per SecAverage Active SessionsHost CPU Usage Per SecConsistent Read Gets Per SecLogons Per SecUser Calls Per SecDatabase Time Per Sec
Physical Reads Per Sec1.0000.5630.4470.2880.5870.9720.1380.4440.2730.4970.4540.4350.6770.7080.6870.5850.3500.3430.708
Physical Writes Per Sec0.5631.0000.2140.1250.4190.6550.1690.3350.2990.4160.6700.6290.4270.4800.4530.3880.0880.0220.480
Hard Parse Count Per Sec0.4470.2141.0000.8090.6000.4090.0370.4890.2070.4880.2650.2660.6490.6380.6340.6160.4150.6810.638
User Rollbacks Per Sec0.2880.1250.8091.0000.4730.255-0.0480.4440.1330.3870.1560.1840.5170.5120.4850.4890.3490.6940.512
Logical Reads Per Sec0.5870.4190.6000.4731.0000.5770.1260.6840.4100.7050.5420.5380.8760.8540.8590.9910.3880.5760.854
I/O Megabytes per Second0.9720.6550.4090.2550.5771.0000.1610.4390.2930.5070.5400.5080.6530.6910.6670.5680.3030.2730.691
DBWR Checkpoints Per Sec0.1380.1690.037-0.0480.1260.1611.0000.1780.4800.2560.2880.2210.1070.1790.1670.124-0.058-0.0130.179
User Commits Per Sec0.4440.3350.4890.4440.6840.4390.1781.0000.4030.6710.5360.5810.7190.7410.7230.6810.3200.5640.741
Enqueue Waits Per Sec0.2730.2990.2070.1330.4100.2930.4800.4031.0000.4110.4930.4490.4160.4830.4530.3940.0800.2300.483
Executions Per Sec0.4970.4160.4880.3870.7050.5070.2560.6710.4111.0000.5530.5530.7280.7470.7320.7050.2110.4400.747
Redo Generated Per Sec0.4540.6700.2650.1560.5420.5400.2880.5360.4930.5531.0000.9050.4970.5640.5290.4940.1200.1440.564
DB Block Changes Per Sec0.4350.6290.2660.1840.5380.5080.2210.5810.4490.5530.9051.0000.4930.5650.5250.4830.1400.1480.565
CPU Usage Per Sec0.6770.4270.6490.5170.8760.6530.1070.7190.4160.7280.4970.4931.0000.9510.9760.8800.4260.7000.951
Average Active Sessions0.7080.4800.6380.5120.8540.6910.1790.7410.4830.7470.5640.5650.9511.0000.9530.8530.4390.6791.000
Host CPU Usage Per Sec0.6870.4530.6340.4850.8590.6670.1670.7230.4530.7320.5290.5250.9760.9531.0000.8600.4350.6650.953
Consistent Read Gets Per Sec0.5850.3880.6160.4890.9910.5680.1240.6810.3940.7050.4940.4830.8800.8530.8601.0000.3940.5960.853
Logons Per Sec0.3500.0880.4150.3490.3880.303-0.0580.3200.0800.2110.1200.1400.4260.4390.4350.3941.0000.5030.439
User Calls Per Sec0.3430.0220.6810.6940.5760.273-0.0130.5640.2300.4400.1440.1480.7000.6790.6650.5960.5031.0000.679
Database Time Per Sec0.7080.4800.6380.5120.8540.6910.1790.7410.4830.7470.5640.5650.9511.0000.9530.8530.4390.6791.000

Missing values

2023-03-23T15:54:50.599318image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-23T15:54:51.122786image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Physical Reads Per SecPhysical Writes Per SecHard Parse Count Per SecUser Rollbacks Per SecLogical Reads Per SecI/O Megabytes per SecondDBWR Checkpoints Per SecUser Commits Per SecEnqueue Waits Per SecExecutions Per SecRedo Generated Per SecDB Block Changes Per SecCPU Usage Per SecAverage Active SessionsHost CPU Usage Per SecConsistent Read Gets Per SecLogons Per SecUser Calls Per SecDatabase Time Per Sec
BEGIN_TIME
2022-01-18 23:58:261040.935383122.1781522.89208411.188233154055.37927610.3949760.462733141.82779728.63989419780.0694104.148875e+051769.327384301.3696933.879906774.136506150213.3201126.32953213425.169393387.990631
2022-01-18 23:59:2712549.8992271819.4994965.71044714.108163276963.856231128.1995300.940544149.16022848.10211621285.0856577.761466e+0658089.654014442.2290787.6871221205.693651184386.3621109.64057814033.758818768.712205
2022-01-19 00:00:269606.6479711721.9729465.65819916.859122391931.689211168.2942920.643352177.91158052.04553027559.1059061.211130e+0790884.295612542.9771089.6154921310.640053231994.7047185.50973314894.044870961.549249
2022-01-19 00:01:273311.882353245.1932775.32773119.042017152066.21848737.0756300.722689179.93277343.89916022191.2941189.614030e+054837.731092358.9957715.457839860.605042145772.0168074.73949616483.781513545.783904
2022-01-19 00:02:263201.800760185.4121924.64232617.231125150466.74376365.6038330.826037165.47166735.86651221002.0816124.902696e+052005.104907348.8528005.7148461008.111680147427.3583350.75995415481.926318571.484649
2022-01-19 00:03:271086.460608144.6833532.41894820.964220178012.09474250.9659001.545439155.92138445.00252020691.1137245.042981e+052063.413405319.5606534.892850991.718461173308.4831184.24995814362.489501489.285004
2022-01-19 00:04:2649554.0332051001.5931583.60556816.652692212992.017441424.0147583.521717151.86986450.98105019396.4447432.909274e+062066.258595527.4306768.5181261341.791045208727.2849246.50679213152.641288851.812583
2022-01-19 00:05:2649978.6693082463.7609382.74063113.554565221270.827142521.6608880.660393144.46095467.50866819182.3509992.668733e+0718912.068681534.2943928.9662471233.250784200960.7396405.36569312018.276374896.624720
2022-01-19 00:06:2750296.3255031936.9295302.53355714.899329222612.030201456.5771810.587248129.24496662.85234918681.2416112.548412e+0721826.258389575.4085899.2963561286.057047200538.0033561.29194612083.708054929.635612
2022-01-19 00:07:2651088.2139922771.7695473.88477414.699588183556.905350524.4609050.329218137.95884854.83127618863.6213992.192927e+0715784.197531593.7522709.2811921279.555556165636.5596716.41975312994.452675928.119162
Physical Reads Per SecPhysical Writes Per SecHard Parse Count Per SecUser Rollbacks Per SecLogical Reads Per SecI/O Megabytes per SecondDBWR Checkpoints Per SecUser Commits Per SecEnqueue Waits Per SecExecutions Per SecRedo Generated Per SecDB Block Changes Per SecCPU Usage Per SecAverage Active SessionsHost CPU Usage Per SecConsistent Read Gets Per SecLogons Per SecUser Calls Per SecDatabase Time Per Sec
BEGIN_TIME
2022-03-14 23:49:2613573.765891161.4990922.4764746.323262275337.642397108.7171872.096748165.75862614.8423316366.567608402607.5614991757.041440605.1663838.6580701185.917121272277.9924053.33498411657.272577865.807022
2022-03-14 23:50:277350.05035284.0382682.0308838.106747449931.36958759.2145022.198724156.79758312.5041966594.075193380597.5159451667.908694645.3143058.0180551349.882511447709.9697892.01409911927.777778801.805487
2022-03-14 23:51:266776.12416183.3221481.3758397.466443253525.75503454.6644302.432886149.61409412.6174505674.328859376576.7785231584.244966540.3483786.9333071180.234899251011.8288591.97986610575.822148693.330661
2022-03-14 23:52:264433.97160899.7193791.2710476.190162200935.47375436.4806872.971278141.54836612.4793666298.679432383122.0204691516.639155499.2021106.3981131006.272697198647.9201063.21888411160.019809639.811317
2022-03-14 23:53:276003.540268106.0738262.7013427.869128207358.13758448.7416110.989933151.0234908.7416116496.845638402484.3624161643.993289514.4957186.9095281169.110738205136.9630875.87248311714.496644690.952760
2022-03-14 23:54:268120.699901132.2713771.4691326.718389287542.604820108.7817761.881809138.59359526.7910206242.538792420703.0042921609.722681520.3646987.0006641021.706834285322.9943885.05117210914.278640700.066392
2022-03-14 23:55:279069.676120418.9964761.4599776.343346269653.06259475.3985572.198355142.64138335.7945965849.236449475974.4923641732.555798568.9502057.5165201316.932371266810.7400573.57442510931.448230751.652002
2022-03-14 23:56:265383.719758107.9637101.0416676.199597302707.91330644.0356182.100134138.22244644.0020166015.742608433960.0134411701.881720543.0197707.0522881159.442204298996.2197582.60416711200.151210705.228842
2022-03-14 23:57:265573.279419118.8149861.2873419.291962231318.13830745.6841061.320350137.26687642.9773897217.758706408141.5415081771.645486517.5720007.5157261108.219178228151.1965674.40666813047.598614751.572578
2022-03-14 23:58:264055.89372874.0373301.0593589.500589240131.10812233.1595762.219607118.14360265.8819575686.951404316929.0734821377.047251459.8561565.855503986.446948238166.5545653.2117039752.177569585.550326